STATISTICAL MODELS FOR ESTIMATING RESILIENCE

Abstract One important component of the Resilience paradigm is the degree of initial change and rapidity of recovery after a stressor. Resilience is determined by both the degree of response, if any, to an insult and, subsequently, the process of return to the initial state. Modeling the recovery process requires use of longitudinal analysis which can incorporate the underlying non-linear trajectory of change, and the multiple systems impacted by the stressor. We will show the models we have employed for one common stressor, surgery. First, we will define and show findings on the Expected Predicted Differential (ERD), the difference between predicted and observed recovery. Second, we will show multivariate trajectories of recovery to demonstrate methods to (1) define typologies of recovery, using PCA, and (2) typologies of individuals in type of recovery, using latent class analysis of trajectories. Second, given the state of the emerging art in resilience research, we will provide a statistician’s view on enhancing the resilience research through design (increasing number and timing of measurements, the number of subjects, and expanding use of the EHR), data structures (incorporating measurements from a greater number of diverse sources, using timing of measures to allow for assessment of dynamic mediation and moderation effects in defining the process), and analysis - extending the ERD to modeling non-linear recovery, incorporating causal analysis, including dynamic mediating and moderating effects into the analytic structure, use Area Under the Curve as a metric for recovery, latent class analysis, and use of multivariate trajectory analysis.

is typically quantified as the degree of recovery in physical/cognitive/psychological functions after the stressor.The simplistic approach of regressing change versus baseline yields biased estimates due to mathematical coupling and regression-to-the-mean.Correction is necessary for eliminating the bias and drawing valid inferences regarding the effect of covariates and baseline status on pre-post change.We present a simple method to correct this bias.We extend the method to include covariates.Our approach considers a counterfactual control group and involves sensitivity analyses to evaluate different settings of control group parameters.We illustrate the method using a large, registry of older adults (N=7,239) who underwent total knee replacement (TKR).We demonstrate how external data can be utilized to constrain the sensitivity analysis.Our analysis indicates that baseline (pre-stressor) function was not strongly linked to recovery after TKR.Among the covariates, only age had a consistent effect on post-stressor recovery.A main takeaway of our work is that studies of resilience should consider, either directly or indirectly, the use of an appropriate control group.Physical resilience -rebound in relevant functioning and biomarkers following a health stressor -is hypothesized to be rooted in the level of fitness of stress-response physiology defining one's "physiological resilience capacity" (PRC).This physiology forms a dynamical system comprising specific modules (the individual stress response systems and their underlying components) and their dynamic interactions with each other via feedback and other protocols.Such a system can be modeled using differential equations whose parameters may then define the PRC.We do not yet know how to measure these parameters directly, however.Rather, they are conceptually defined "constructs" which must be inferred using indirect measures-ideally, stimulus response data probing multiple aspects of the relevant physiology.Latent variable models are ideally suited to this setting.Two challenges for their application in studies of resilience are presented: (1) Integrating specific scientific knowledge on the dynamical systems in modeling the co-distribution of the indirect measures.(2) Synergizing such models' advantages for construct measurement with advantages of machine learning approaches to optimize accuracy for predicting resilient outcomes by inferred PRC.Challenges and their proposed solutions are illustrated using multifaceted stimulus response data being collected in an ongoing investigation on the physiological basis of resilience to clinical stressors in older adults, the Study of Physical Resilience in agING.The work aims to produce physiologically rooted measures providing effective risk predictors for older adults facing impending stressors as well as intervention candidates by which to promote PRC in the longer term.

APPROACHES TO QUANTITATIVELY EXAMINE RESILIENCE IN OLD AGE
Rene Melis 1 , Almar Kok 2 , and Martijn Huisman 2 , 1. Radboud University Medical Center, Nijmegen, Gelderland, Netherlands, 2. Amsterdam University Medical Center, Amsterdam, Noord-Holland, Netherlands Resilience is a complex system's ability to maintain or recover in function following a perturbation.The concept is generic and can be applied to any complex system going from a cell, a person, to their support networks.By applying a resilience approach, we can better understand why people respond differently to a similar exposure.While most people have an intuitive understanding of what resilience is, it's definition and application is not straightforward.Therefore, it is important to carefully define the stressor, system and (functional) outcome(s) and how it is studied using e.g., the TransNIH resilience framework.Quantitative examination of resilience can focus on predictors, mechanisms, trajectories and/or outcomes and each aspect requires a different analytical approach.This talk will review different quantitative approaches to examine resilience in the context of aging.These approaches can be categorized based on the statistical methods that are used to operationalize resilience: the effect modification approach to study how hypothesized resilience factors modify the outcome following perturbation, scale construction in the psychometric approach, comparison of characteristics between groups based on predefined prospective resilience responses in the a priori approach, datadriven subgroup identification based on resilience outcome (trajectories) in the clustering approach, analyzing predictors of adversity-outcome residual values in the residual approach, and analyzing stressor-response patterns in intensive longitudinal data to better understand resilience mechanisms.The approaches are not mutually exclusive.Researchers may choose to combine multiple approaches and may analyze the same data using multiple approaches to compare the findings between them.One important component of the Resilience paradigm is the degree of initial change and rapidity of recovery after a stressor.Resilience is determined by both the degree of response, if any, to an insult and, subsequently, the process of return to the initial state.Modeling the recovery process requires use of longitudinal analysis which can incorporate the underlying non-linear trajectory of change, and the multiple systems impacted by the stressor.We will show the models we have employed for one common stressor, surgery.First, we will define and show findings on the Expected Predicted Differential (ERD), the difference between predicted and observed recovery.Second, we will show multivariate trajectories of recovery to demonstrate methods to (1) define typologies of recovery, using PCA, and (2) typologies of individuals in type of recovery, using latent class analysis of trajectories.Second, given the state of the emerging art in resilience research, we will provide a statistician's view on enhancing the resilience research through design (increasing number and timing of measurements, the number of subjects, and expanding use of the EHR), data structures (incorporating measurements from a greater number of diverse sources, using timing of measures to allow for assessment of dynamic mediation and moderation effects in defining the process), and analysis -extending the ERD to modeling non-linear recovery, incorporating causal analysis, including dynamic mediating and moderating effects into the analytic structure, use Area Under the Curve as a metric for recovery, latent class analysis, and use of multivariate trajectory analysis.

MECHANISMS UNDERLYING SLEEP AND DEVELOPMENT OF CHRONIC CONDITIONS IN OLDER ADULTS
Chair: Christopher Kaufmann Co-Chair: Soomi Lee Discussant: Christina McCrae Mounting evidence suggests poor sleep is associated with the development of chronic conditions.Numerous mechanisms for these links have been proposed, including inflammation, metabolic function, and brain structure.In this symposium, we will present five studies focused on links between poor sleep and subsequent development of chronic disease across adulthood.Specifically, we will examine this relationship at multiple levels from the cellular to the clinical level with the ultimate goal of identifying potential mechanisms and consequences of links between poor sleep and chronic conditions.Paper 1 will identify within-person patterns of various sleep experiences and link them to incident chronic conditions over a 10-year period.Paper 2 will examine associations between sleep parameters, measured via wrist actigraphy, and levels of adipokines, which are adipose-derived cytokines associated with development of metabolic disease.In Paper 3, we will shift our focus to examine the association between poor sleep and inflammatory biomarkers, specifically C-reactive protein, circulating and lipopolysaccharide-stimulated cytokines.Paper 4 will assess the impact of poor sleep health on fear of falling and actual falls.Paper 5 will focus on the effects of poor sleep and arousal with cognition and brain structure, specifically within a population of patients with chronic pain.As Discussant, Dr. Christina McCrae will integrate these findings and suggest directions for future research.In summary, our symposium will elucidate biological and psychological mechanisms linking poor sleep to manifestation of chronic disease.Better understanding of these mechanisms will inform the development of interventions to promote successful aging.

SLEEP TIGHT, STAY HEALTHY? MULTIDIMENSIONAL SLEEP HEALTH AND CHRONIC CONDITION DEVELOPMENT OVER ONE DECADE
Claire Smith 1 , and Soomi Lee 2 , 1. University of South Florida, Tampa, Florida, United States, 2. The Pennsylvania State University, University Park, Pennsylvania, United Sta tes Chronic conditions affect half of adults and are the leading cause of death in the United States.Healthy sleep is a modifiable risk factor, but greater information is needed about specific sleep experiences linked to chronic condition development.The present study aims to (1) identify withinperson patterns of various sleep experiences (i.e., sleep health phenotypes describing sleep duration, regularity, sleep onset latency, insomnia symptoms, daytime tiredness, and nap frequency) and ( 2) understand the connection between transitions in sleep health phenotypes and chronic condition development.We use self-report data from a national sample of adults (N=5,026) taken from the Midlife in the United States (MIDUS) study across two waves (II and III) separated by about ten years.Latent transition analysis revealed four sleep phenotypes: good sleepers (optimal sleep across dimensions), insomnia sleepers (short duration, long sleep onset latency, high daytime tiredness, frequent insomnia symptoms), weekend catch-up sleepers (long sleep duration on weekends compared to weekdays), and nappers (frequent naps).For cardiovascular conditions, consistent insomnia sleepers (i.e., insomnia sleeper -> insomnia sleeper) were at 72% higher risk (CI:[1.04,2.82]) than consistently good sleepers.For diabetes, consistent insomnia sleepers again were at higher risk (188%; CI:[1.72,4.79])as were consistent nappers (128%; CI:[1.26,4.09])and weekend catch-up sleepers -> nappers (137%; CI:[1.21,4.60]).For cancers, only nappers -> insomnia sleepers exhibited higher risk (45%; CI:[1.16,1.83]).We newly identify two sleep health phenotypes (napper and, especially, insomnia sleeper) as risks for developing life-threatening chronic conditions.

ASSOCIATIONS OF ACTIGRAPHIC SLEEP WITH ADIPOKINES IN OLDER ADULTS
Yiwei Yue 1 , Zhikui Wei 2 , Jill Rabinowitz 1 , Chee Chia 3 , Toshiko Tanaka 3 , Eleanor Simonsick 3 , Luigi Ferrucci 3 , and Adam Spira 1 , 1. Johns Hopkins Bloomberg School of  Public Health, Baltimore, Maryland, United States, 2. Johns  Hopkins School of Medicine, Baltimore, Maryland, United  States, 3. National Institute on Aging, National Institutes of  Health, Baltimore, Maryland, United States  Adipokines are adipose-derived cytokines that play important roles in metabolism and obesity.Accumulating evidence suggests that sleep disturbance could alter adipokine levels and contribute to systemic metabolic dysfunction in younger and middle-aged populations.Less is known about links between sleep disturbance and adipokines in older adults.We examined cross-sectional associations of actigraphic sleep parameters with adipokine levels in 353 community-dwelling older adults aged 74.8±8.3 years (51.3% male, 26.6% non-white) enrolled in the Baltimore Longitudinal Study of Aging.Participants completed 6.56±1.06nights of wrist actigraphy and had morning fasting adipokine levels measured in plasma.Predictors included actigraphic total sleep time, sleep onset latency, wake after sleep onset, sleep efficiency, and average wake bout length (WBL), and outcomes were levels of the adipokines,